The Impact of Image Spatial Resolution and Machine Learning Algorithm on Urban Vegetation Classification: Focus on Data Loss and Misclassification by Alexander Takele Muleta, Julius Bamah, Shirley Bushner, Oz Kira

“…This research investigates the classification efficiency of various satellite resolutions and machine learning algorithms, assessing the impact of spatial resolution on urban vegetation classification using WorldView-2 imagery resampled from 0.5 to 5 and 10 m. …”
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XGBoost-based machine learning model combining clinical and ultrasound data for personalized prediction of thyroid nodule malignancy by Wenhan Li, Wenhan Li, Yajing Zhou, Ziyu Luo, Ziyu Luo, Miao Tan, Miao Tan, Rui Yin, Jianhui Li, Jianhui Li

“…PurposeThyroid ultrasound is a primary tool for screening thyroid nodules (TNs), but existing risk stratification systems have limitations. Nowadays, machine learning (ML) offers advanced capabilities to handle high-dimensional data and complex patterns. …”
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Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data by Wanting Xiang, Gang Zhu, Yichong Hou, Zhandong Mei, Lin Wan, Li Zhang, Guang Yang, Jian Zu

“…A two-stage progressive diagnosis framework based on EEG data and machine learning methods was developed. …”
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Enhancing Immunoglobulin G Goat Colostrum Determination Using Color-Based Techniques and Data Science by Manuel Betancor-Sánchez, Marta González-Cabrera, Antonio Morales-delaNuez, Lorenzo E. Hernández-Castellano, Anastasio Argüello, Noemí Castro

“…A total of 813 colostrum samples were collected in a previous study (June 1997–April 2003) that utilized multiple regression analysis as a reference to verify that applying data science techniques improves accuracy and reliability. …”
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An approach for accurate identification and monitoring of species in mangrove forests based on multi-source spectral data and deep learning by Monterrubio-Martínez Erandi, Trujillo-Acatitla Rubicel, Tuxpan-Vargas José, Moreno-Casasola Patricia

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Deductively coding psychosocial autopsy interview data using a few-shot learning large language model by Elias Balt, Elias Balt, Salim Salmi, Sandjai Bhulai, Stefan Vrinzen, Merijn Eikelenboom, Merijn Eikelenboom, Renske Gilissen, Renske Gilissen, Daan Creemers, Daan Creemers, Arne Popma, Saskia Mérelle, Saskia Mérelle

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Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM by Widya Utama, Maman Hermana, Dwa D. Warnana, Wien Lestari, Muhammad N. A. Zakariah, Sherly A. Garini, Rista F. Indriani, Dhea P. Novian Putra, M Ulin Nuha Abduh, Alif N. F. Insani, Dandi Syahtia Pratama, Khairul Arifin Mohd Noh, Abdul Halim Abdul Latiff

“…The proposed models utilize MT data, incorporating electric field (E) and magnetic field (H) components, to effectively reduce noise. …”
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Machine learning approaches for predicting frailty base on multimorbidities in US adults using NHANES data (1999–2018) by Teng Li, Xueke Li, Haoran XU, Yanyan Wang, Jingyu Ren, Shixiang Jing, Zichen Jin, Gang chen, Youyou Zhai, Zeyu Wu, Ge Zhang, Yuying Wang

“…Key impacting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary Heart Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. …”
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Gray and white matter alterations in Obsessive-Compulsive Personality Disorder: a data fusion machine learning approach by Lorenzo Arena, Wenceslao Peñate, Wenceslao Peñate, Francisco Rivero, Rosario J. Marrero, Rosario J. Marrero, Teresa Olivares, Alessandro Scarano, Ascensión Fumero, Ascensión Fumero, Alessandro Grecucci

“…One intriguing hypothesis is that regions ascribed to the Default Mode Network are involved in OCPD, similar to what has been shown for OCD and other anxiety disorders.MethodsTo test this hypothesis, the gray and white matter images of 30 individuals diagnosed with OCPD (73% female, mean age=29.300), and 34 non-OCPD controls (82% female, mean age = 25.599) were analyzed for the first time with a data fusion unsupervised machine learning method known as Parallel Independent Component Analysis (pICA) to detect the joint contribution of these modalities to the OCPD diagnosis.ResultsResults indicated that two gray matter networks (GM-05 and GM-23) and one white matter network (WM-25) differed between the OCPD and the control group. …”
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Data mining in occupational safety and health: a systematic mapping and roadmap by Beatriz Lavezo dos Reis, Ana Caroline Francisco da Rosa, Ageu de Araujo Machado, Simone Luzia Santana Sambugaro Wencel, Gislaine Camila Lapasini Leal, Edwin Vladimir Cardoza Galdamez, Rodrigo Clemente Thom de Souza

“…Abstract Paper aims This research presents a literature overview in relation to data mining and machine learning applications in the area of occupational health and safety. …”
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An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data by F. Quispe-Huaynasi, F. Roig, N. Holanda, V. Loaiza-Tacuri, Romualdo Eleutério, C. B. Pereira, S. Daflon, V. M. Placco, R. Lopes de Oliveira, F. Sestito, P. K. Humire, M. Borges Fernandes, A. Kanaan, C. Mendes de Oliveira, T. Ribeiro, W. Schoenell

“…In this context, we present an unsupervised machine learning approach to identify candidates for spectroscopic follow-up using data from the Southern Photometric Local Universe Survey (S-PLUS). …”
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Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data by Renjie Lu, Shiyun Wang, Pinghua Chen, Fangfang Li, Fangfang Li, Pan Li, Qian Chen, Xuefei Li, Fangyu Li, Suxia Guo, Jinlin Zhang, Jinlin Zhang, Dan Liu, Zhijun Hu

“…Model accuracy was evaluated using calibration curves, while predictive performance was assessed through receiver operating characteristic (ROC) and decision curve analysis (DCA). Four machine learning algorithms were utilized, with the optimal model undergoing hyperparameter optimization to evaluate the significance of predictive factors.ResultsA total of 1,092 CKD patients were included, with 231 (21.2%) diagnosed with sarcopenia. …”
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Technical note: Reconstructing missing surface aerosol elemental carbon data in long-term series with ensemble learning by Q. Meng, Y. Zhang, S. Zhong, J. Fang, L. Tang, Y. Rao, M. Zhou, J. Qiu, X. Xu, J.-E. Petit, O. Favez, X. Ge

“…This study proposed an ensemble learning modeling method to address these challenges. …”
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Artificial intelligence in multimodal learning analytics: A systematic literature review by Mehrnoush Mohammadi, Elham Tajik, Roberto Martinez-Maldonado, Shazia Sadiq, Wojtek Tomaszewski, Hassan Khosravi

“…The proliferation of educational technologies has generated unprecedented volumes of diverse, multimodal learner data, offering rich insights into learning processes and outcomes. …”
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A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors by Ahmad Aminzadeh, Sasan Sattarpanah Karganroudi, Soheil Majidi, Colin Dabompre, Khalil Azaiez, Christopher Mitride, Eric Sénéchal

“…This research combines updated concepts from the Internet of Things, machine learning, multi-sensor data collection, structured data mining, and cloud-based data analysis. …”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…Traditional modeling methods face challenges due to the increasing complexity and volume of data. Deep learning (DL) has emerged as a promising tool, offering significant improvements in accuracy and efficiency for tasks such as time series prediction, spatial data analysis, and inverse modeling. …”
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Applications of Machine Learning Algorithms in Geriatrics by Adrian Stancu, Cosmina-Mihaela Rosca, Emilian Marian Iovanovici

“…The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. …”
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Image Classification of Imbalanced Wood Microscope by Integrating Multi-source Features by Zhikang Tian, Na Zhang, Jiwei Wang, Liwei Sha, Hongping Liu, Li Zou

Subjects: “…Wood image analysis…”
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Soziale Ungleichheiten in der Beschäftigung mit nachhaltigkeitsbezogenen Themen im Erwachsenenalter by Jana Costa, Claudia Kühn

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Analyzing the impact of social media sentiments on government response during natural disasters in Pakistan by Fazal Tariq, Muhammad Tufail, Taj Rehman

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